A major emphasis in recent Alzheimer's disease (AD) research is identifying features and avenues for early intervention in the preclinical state of AD. This is essential because by the time a patient exhibits symptomatic dementia, millions of neurons in the brain have already been lost. Advanced imaging methods that enable visualization of AD associated brain changes at an early stage are likely to play an important role in detecting the onset of neurodegeneration early, prior to extensive neural loss. In parallel, automated image analysis algorithms must be developed to enable not only accurate localization and characterization of changes but also identify patterns from the data to enable individual subject diagnosis. Recent results suggest that this may indeed be possible using machine learning techniques: sensitivity and specificity of around 85% have been obtained by several groups using either T1-weighted MR (Magnetic Resonance), FDG PET (18fluorodeoxyglucose Positron Emission Tomography), or other modalities. The availability of multiple imaging modalities for longitudinally followed subjects within the ADNI data now offers the opportunity of significant improvements in sensitivity/specificity of diagnostic tools for AD. This can be achieved by developing new mathematical models to leverage multiple imaging modalities and longitudinal data in conjunction-for accurate diagnosis as well as to aid in analyzing the effect of new treatments, as they become available. Hypothesis: Significant improvements in accuracy (sensitivity/specificity) for discriminating AD, MCI (Mild Cognitive Impairment), and healthy controls (at the level of individual subjects) are possible by making use of multiple imaging modalities and longitudinal data. Specific Aims: (1) To develop new image-based classification algorithms that can take advantage of multiple imaging modalities simultaneously within a unified framework;and to extensively evaluate these methods on the entire ADNI image dataset. (2) To extend our algorithms to incorporate longitudinal image data for further improvements and perform evaluation on ADNI image data. (3) To design and disseminate an open source software system to assist in AD diagnosis. Methods: We have recently developed a new machine learning based algorithm for AD classification that gives 85% sensitivity/specificity in correctly identifying AD patients from healthy controls using T1-weighted MR images from ADNI, and over 80% classification sensitivity/specificity using FDG PET images. Building on these efforts, we will extend and improve our models to utilize multi-modal image data (Specific Aim 1). The algorithms will be further augmented with longitudinal data to enable more accurate prediction for difficult to diagnose cases (Specific Aim 2). We will extensively evaluate the stepwise improvements of our method on the ADNI dataset and perform statistical evaluations of the relationship of measures such as the classification confidence with various cognitive/laboratory biomarkers available within ADNI. These algorithms will be implemented and disseminated via an easy to use software system to facilitate AD diagnosis (Specific Aim 3). PUBLIC HEALTH RELEVANCE: The proposed research will develop novel algorithms and software systems for highly accurate Alzheimer's disease diagnosis. The techniques will take advantage of the individual's brain images from two or more imaging modalities-such as structural images (e.g., Magnetic Resonance) and functional images (e.g., Positron Emission Tomography). The algorithms and software as a result of successful completion of this project will combine the multi-modal image information with the longitudinal data of the patient (if available) to provide invaluable assistance in AD diagnosis at the level of individual subjects.